Apport de l’imagerie aéroportée par drone dans l’étude de l’interaction machine-sol-plante en culture de pomme de terre

Abstract

The use of new technologies in precision agriculture remains a solution for global demographic change and its food needs in the face of climate change. The experiment was based on the vegetative cycle monitoring of a "Spunta" variety potato crop grown at different inter-lines, spacing and planting depths based on the RGB imaging technique using a sophisticated drone-mounted sensor. In order to improve the production system and adapt it to the context of global warming through the different settings on the potato planter machine, vegetation indices have been calculated from the captured images such as the GA, GGA, CSI, NGDRI et TGI indices, indicators of the plant biomass and its healthy state thus leading to a correct decision in terms of nutrient input and phytosanitary treatment. The following factors combination of inter-row spacing, plant spacing and planting depth IL = 90 cm, IP = 28 cm, and P = 10 cm respectively proved potato size and yield better results.

Artificial intelligence applications in precision agriculture to predict the effect of Root-Knot nematodes and grafting on vegetable crop health from proximal remote sensing machines

“…2020). Remote sensing with the use of high-resolution multispectral images is one of the techniques that has been employed (Abrougui et al 2022b). Therefore, it is necessary to take over that high populations of the nematode beget a meaningful reduction of the crop biomass, which allows the employment of vegetation indexes that offer a good correlation with the biomass to indicate places with a lesser probability of circumstance of nematodes.…”